Dynamic Trees for Learning and Design (dynaTree R package)
dynaTree is an R package implementing sequential Monte Carlo inference for dynamic tree regression and classification models by particle learning (PL). The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design and optimization. The current version supports
- regression by constant and linear leaf models
- classification by multinomial leaf models
- sequential design for regression models by active learning heuristics including predictive variance (ALM) and the expected reduction in predictive variance (ALC)
- optimization of regression models by expected improvement (EI) statistics
- sequential exploration of classification boundaries by the predictive entropy
- Obtain R from cran.r-project.org by selecting the version for your operating system.
- Install the dynaTree package, from within R.
- Optionally, install the akima, plgp and tgp packages, which
are helpful for some of the comparisons in the examples and demos.
> install.packages(c("akima", "plgp", "tgp"))
- Load the library as you would for any R library.
- See the package documentation. A pdf version of the
reference manual, or help pages, as also available.
The help pages can be accessed from within
R. The best way to acquaint yourself with the functionality
of this package is to run the demos which illustrate the examples
contained in the papers referenced below. Try starting with...
> ?dynaTree # follow the examples
> demo(package="dynaTree") # for a listing of the demos
Please send questions and comments to bobby_AT (_statslab_DOT_cam_DOT_ac_DOT_uk). Enjoy!
Robert B. Gramacy -- 2010